Published: 2022-07-04
Prediksi Tingkat Pengangguran Berdasarkan Data Time Series Menggunakan Regresi Linear (Studi Kasus : Kota Salatiga)
DOI: 10.35870/emt.v6i2.702
Dio Yudha Perdana, Magdalena A. Ineke Pakereng
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Dio Yudha Perdana:
Satya Wacana Christian University
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Magdalena A. Ineke Pakereng:
Satya Wacana Christian University
Abstract
Unemployment is a very big problem. Coupled with the outbreak of the COVID-19 virus which resulted in many employees and workers being laid off. The purpose of this study is to predict the unemployment rate based on the number of the workforce. To produce an accurate prediction requires valid data in the long term. This makes it a reference for forecasting the unemployment population in a 5-year time series. For forecasting, a method is needed, namely the Linear Regression method. Linear regression is a statistical method that serves to test how far the relationship between the causal variables and the effect variables is. After forecasting, prediction data is generated in 2022, and the population and unemployment have changed from the previous year. Where the population in 2022 is predicted to be 144189 and the number of unemployed is 4513 people. This result is a prediction, not 100% accurate, therefore it would be better if you use another method to compare the prediction results and the level of accuracy.
Keywords
Article Information
This article has been peer-reviewed and published in the Jurnal EMT KITA. The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 6 No. 2 (2022)
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Section: Articles
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Published: 2022-07-04
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License: CC BY 4.0
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Copyright: © 2022 Authors
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DOI: 10.35870/emt.v6i2.702
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Dio Yudha Perdana, Satya Wacana Christian University
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia
Magdalena A. Ineke Pakereng, Satya Wacana Christian University
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia
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Al’afi, A. M., Widiarti, W., Kurniasari, D., & Usman, M. (2020). Peramalan Data Time Series Seasonal Menggunakan Metode Analisis Spektral. Jurnal Siger Matematika, 1(1), 10-15.
-
Ashari, M. L., & Sadikin, M. (2020). Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi Lstm. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 9(1), 1-10.
-
C. Nurine S. K (2018). “Analisis Pengaruh Indeks Pembangunan Manusia, Tingkat Pengangguran, Pertumbuhan Ekonomi, dan Upah Minimum Provinsi Terhadap Tingkat Kemiskinan (Studi Kasus pada 6 Provinsi di Pulau Jawa)”, Fakultas Ekonomi dan Bisnis. Universitas Brawijaya.
-
F. S. Nur Ravika. (2016). “Pengaruh Variabel Ekonomi Makro Terhadap Pengangguran Terdidik Jawa Timur Tahun 2010-2014. Surabaya : PT.Bumi Universal Makmur”, Jurnal Ekonomi Pembangunan Vol.14, No.01 Juni 2016.
-
Fadlillah, Nurul. (2016). “Analisis Pengaruh Pendapatan Per Kapita, Tingkat Pengangguran, IPM dan Pertumbuhan Penduduk Terhadap Kemiskinan di Jawa Tengah Tahun 2009-2013”, Fakultas Ekonomi dan Bisnis, Universitas Jenderal Soedirman. EKO-REGIONAL, VOL.11, No.1.
-
I. H. Andrian. “Penerapan Metode Fuzzy Time Series Untuk Prediksi Penjualan Berbasis WEB pada CV.AGVA Kota Pasuruan”. Teknik Informatika, ITN Malang, Malang.
-
Ikhlas, M., & Adipradana, W. (2020). Peramalan Beban Pada Tahun 2019-2023 Menggunakan Metode Time Series Pada Penyulang Dieng Di Gardu Induk Simpang Tiga Indralaya (Doctoral dissertation, Sriwijaya University).
-
Katemba, P., & Djoh, R. K. (2017). Prediksi Tingkat Produksi Kopi Menggunakan Regresi Linear. Jurnal ilmiah FLASH, 3(1), 42-51.
-
N. Shofiatin. (2018). “Pengaruh Jumlah Penduduk dan Jumlah Pengangguran Terhadap Jumlah Penduduk Miskin di Provinsi Banten”, Fakultas Ekonomi dan Bisnis Islam. Universitas Islam Negeri Sultasn Maulana Hasanuddin Banten.
-
Nidyashofa, Nurika. (2020). “Pemilihan Model Regresi Spasial Terbaik Pada Tingkat Pengangguran Terbuka Di Provinsi Jawa Tengah”. Program Studi S1 Statistika. Fakultas Matematika Dan Ilmu Pengetahuan Alam. Universitas Muhammadiyah Semarang. Semarang.
-
Oktavianti, I., Ermatita, E., & Rini, D. P. (2019). Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 3(2), 282-287.
-
P. Ratih. (2016). “Pengangguran dan Pengaruhnya terhadap Tingkat Kemiskinan. Balai Besar Penelitian dan Pengembangan Pelayanan Kesejahteraan Sosial”, Jurnal PKS Vol 15 No 2 Juni 2016; 89-100.
-
R. A. Salbiah and W. Adi. (2016). “Faktor-faktor yang mempengarhui tingkat kemiskinan”, Fakultas Ekonomi dan Bisnis, Universitas Mulawarman, Samarinda. JIEM – Volume 1(1), 2016.
-
Rahakbauw, dkk. (2018). “Sistem Prediksi Tingkat Pengangguran Di Provinsi Maluku Menggunakan ANFIS (Adaptive Neuro Fuzzy Inference System)”. Jurusan Matematika FMIPA. Universitas Pattimura. Ambon.
-
S. Dwi (2018). “Penyebab Tingginya Angka Pengangguran Di Kota Metro”, Fakultas Ekonomi Dan Bisnis Islam. Institut Agama Islam Negeri Metro.
-
Sandy, dkk. (2019). “Analisa Pengaruh Pendapatan Asli Daerah dan Dana Desa Dalam Menurunkan Tingkat Kemiskinan Di 11 Kabupaten Provinsi Sulawesi Utara”. Jurusan Ekonomi Pembangunan, Fakultas Ekonomi dan Bisnis. Universitas Sam Ratulangi. Manado.
-
Sumardin, A., & Mashud, M. (2018). Penerapan Metode Time Series Dalam Memprediksi Hasil Produksi Pertanian Berdasarkan Nilai Trend. Inspiration: Jurnal Teknologi Informasi dan Komunikasi, 8(1), 25-30.
-
Wahyuni, S. C. (2021). Peramalan Jumlah Penduduk Miskin Di Pulau Jawa Menggunakan Metode Fuzzy Time Series Chen (Doctoral dissertation, UNIVERSITAS MUHAMMADIYAH JEMBER).
-
Yunus, Erna. (2022). “Angka Kemiskinan Salatiga 2021 Naik 5,14%”. Angka Kemiskinan Salatiga 2021 Naik 5,14% - RMOL JATENG (rmoljawatengah.id). Diakses pada 30 April 2022.
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